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| Title |
Elective Course in Computer Science: Deep Learning
|
| Semester |
E2023
|
| Master programme in |
Computer Science
|
| Type of activity |
Course |
| Teaching language |
English
|
| Study regulation |
You register for activities through stads selvbetjening during the announced registration period, which you can see on the Study administration homepage. When registering for courses, please be aware of the potential conflicts and overlaps between course and exam time and dates. The planning of course activities at Roskilde University is based on the recommended study programmes, which should not overlap. However, if you choose optional courses and/or study plans that goes beyond the recommended study programmes, an overlap of lectures or exam dates may occur depending on which courses you choose. |
| REGISTRATION AND STUDY ADMINISTRATIVE | |
| Registration |
Read about the Master Programme and find the Study Regulations at ruc.dk |
| Number of participants |
|
| ECTS |
5
|
| Responsible for the activity |
Henning Christiansen (henning@ruc.dk)
|
| Head of study |
Henrik Bulskov (bulskov@ruc.dk)
|
| Teachers |
|
| Study administration |
IMT Registration & Exams (imt-exams@ruc.dk)
|
| Exam code(s) |
U60598
|
| ACADEMIC CONTENT | |
| Overall objective |
The purpose of elective courses is to give the student opportunitities to specialize within a specific subject area, where the student acquires knowledge, skills and competences in order to translate theories, methods and solutions ideas into their own practice. |
| Detailed description of content |
The course includes - Fundamental concepts of Machine Learning and Artificial Neural Networks. - Deep learning architectures and tool - Different types of deep networks (for images, text, ...) - Defining deep learning tasks, prepare data, train and deploy deep models. Software tools: Python, TensorFlow, Keras (some familiarity with Python will be an advantage). |
| Course material and Reading list |
François Chollet: Deep learning with Python, Second Edition. Manning, 2021. Course notes and scientific papers made available on moodle. |
| Overall plan and expected work effort |
The course's 5 ECTS correspond to a total of 135 hours workload with:
|
| Format |
|
| Evaluation and feedback |
Evaluation form to be filled out (anonymously) plus open discussion on the last course day. |
| Programme |
|
| ASSESSMENT | |
| Overall learning outcomes |
After completing this course, students will be able to:
|
| Form of examination |
Individual oral exam based on a written product
The character limit of the written product is maximum48,000 characters, including spaces. The character limits include the cover, table of contents, bibliography, figures and other illustrations, but exclude any appendices. Time allowed for exam including time used for assessment: 20 minutes. The assessment is an overall assessment of the written product(s) and the subsequent oral examination. Permitted support and preparation materials for the oral exam: All. Assessment: 7-point grading scale. Moderation: Internal co-assessor. |
| Form of Re-examination |
Samme som ordinær eksamen / same form as ordinary exam
|
| Type of examination in special cases |
|
| Examination and assessment criteria |
|
| Exam code(s) | |
| Last changed | 25/05/2023 |
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